{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.font_manager import FontProperties\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "my = pd.read_csv('maoyanfilms_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>films_id</th>\n",
       "      <th>films_box_office</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>47200.0</td>\n",
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       "      <td>11299.0</td>\n",
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       "      <td>2137.0</td>\n",
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       "      <td>33.0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>14587</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>14588</th>\n",
       "      <td>1301695</td>\n",
       "      <td>256.0</td>\n",
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       "    <tr>\n",
       "      <th>14589</th>\n",
       "      <td>1301739</td>\n",
       "      <td>11.0</td>\n",
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       "    <tr>\n",
       "      <th>14592</th>\n",
       "      <td>1302064</td>\n",
       "      <td>4.0</td>\n",
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       "    <tr>\n",
       "      <th>15080</th>\n",
       "      <td>1309416</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1501 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       films_id  films_box_office\n",
       "0             3           47200.0\n",
       "1             4           11299.0\n",
       "2             5           13700.0\n",
       "3             7            2137.0\n",
       "4            12              33.0\n",
       "...         ...               ...\n",
       "14587   1301685               1.0\n",
       "14588   1301695             256.0\n",
       "14589   1301739              11.0\n",
       "14592   1302064               4.0\n",
       "15080   1309416              54.0\n",
       "\n",
       "[1501 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr1 = my[['films_id','films_box_office']].dropna()\n",
    "fr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>films_id</th>\n",
       "      <th>films_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>7.4</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>6.1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>7.8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
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       "      <th>...</th>\n",
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       "      <th>14374</th>\n",
       "      <td>1298827</td>\n",
       "      <td>7.9</td>\n",
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       "    <tr>\n",
       "      <th>14375</th>\n",
       "      <td>1298835</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14407</th>\n",
       "      <td>1299627</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14467</th>\n",
       "      <td>1299906</td>\n",
       "      <td>7.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15184</th>\n",
       "      <td>1320329</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2520 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       films_id  films_score\n",
       "0             3          7.4\n",
       "1             4          6.1\n",
       "2             5          7.8\n",
       "3             7          7.1\n",
       "4            12          8.5\n",
       "...         ...          ...\n",
       "14374   1298827          7.9\n",
       "14375   1298835          8.0\n",
       "14407   1299627          6.4\n",
       "14467   1299906          7.3\n",
       "15184   1320329          5.0\n",
       "\n",
       "[2520 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr2 = my[['films_id','films_score']].dropna()\n",
    "fr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "fr3 = pd.merge(fr1,fr2,on='films_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>films_box_office</th>\n",
       "      <th>films_score</th>\n",
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       "      <th>1079</th>\n",
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       "      <td>2389.0</td>\n",
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       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>1284949</td>\n",
       "      <td>944.0</td>\n",
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       "      <th>1081</th>\n",
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       "      <td>8.1</td>\n",
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       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>1298827</td>\n",
       "      <td>6271.0</td>\n",
       "      <td>7.9</td>\n",
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       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>1298835</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1084 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      films_id  films_box_office  films_score\n",
       "0            3           47200.0          7.4\n",
       "1            4           11299.0          6.1\n",
       "2            5           13700.0          7.8\n",
       "3            7            2137.0          7.1\n",
       "4           12              33.0          8.5\n",
       "...        ...               ...          ...\n",
       "1079   1278409            2389.0          8.9\n",
       "1080   1284949             944.0          8.5\n",
       "1081   1285928             557.0          8.1\n",
       "1082   1298827            6271.0          7.9\n",
       "1083   1298835            1127.0          8.0\n",
       "\n",
       "[1084 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fr3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#设置图片大小\n",
    "plt.figure(figsize=(20,8),dpi=80)\n",
    "\n",
    "#设置字体\n",
    "my_font = FontProperties(fname=r\"C:\\Windows\\Fonts\\simsun.ttc\", size=14)\n",
    "\n",
    "#绘制图片\n",
    "plt.scatter(fr3['films_score'],fr3['films_box_office'])\n",
    "\n",
    "#添加描述信息\n",
    "plt.xlabel(\"评分\",fontproperties=my_font)\n",
    "plt.ylabel(\"票房\",fontproperties=my_font)\n",
    "plt.title(\"票房与评分的关系\",fontproperties=my_font)\n",
    "\n",
    "#保存图片\n",
    "plt.savefig(\"./票房与评分的关系.png\")\n",
    "\n",
    "#展示图片\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
